The invention relates to adaptive control systems and, in particular, to adaptive control systems for active acoustic attenuation.
Active acoustic attenuation involves injecting a canceling acoustic wave, such as sound or vibration, to destructively interfere with and cancel an input acoustic wave. The output acoustic wave is sensed with an error sensor such as a microphone in a sound system or an accelerometer in a vibration system. An error input signal is supplied to an adaptive control filter, and adaptive parameters in the filter are updated in relation to the error input signal to adapt the filter.
The adaptive control filter model receives a reference or input signal and in turn supplies a correction signal to an output transducer such as a loudspeaker in a sound application or a shaker in a vibration application. The output transducer injects a canceling acoustic wave to destructively interfere with the input acoustic wave so that the output acoustic wave at the error sensor is zero or some other desired value. In a feedforward system, the reference or input signal is obtained using an input sensor located upstream of the canceling transducer. The input sensor can be in a sound system or an accelerometer in a vibration system. In a feedback system, the reference or input signal to the adaptive control filter model is typically an error signal from the error sensor or a signal derived therefrom.
It is important that the adaptive control filter in an active acoustic attenuation system be stable (i.e. converge), and also that the adaptive filter be robust. The filtered-X least-mean-square (LMS) and the filtered-U recursive-least-mean-square (RLMS) update methods as described in U.S. Pat. No. 4,677,676 which is incorporated herein by reference, are effective means of providing adaptive control in many active acoustic attenuation applications. In the filtered-X LMS method, a C model of an auxiliary path after the output of the adaptive control filter (e.g. the speaker-error path in sound applications) filters the reference signal. the filtered reference signal is the regressor to an error correlator which correlates the error signal from the error sensor to generate an error input signal that updates the adaptive control filter. The C modeling of the auxiliary path can be accomplished off-line, or preferably adaptively on-line such as described in the above incorporated U.S. Pat. No. 4,677,676. The filtered-U RLMS method can be accomplished in a similar fashion as disclosed in U.S. Pat. No. 4,677,676.
Delayed inverse C modeling is another method for implementing the LMS update. In that method, the error signal is filtered through an inverse of a delayed C model, and the reference signal is delayed to generate the regressor to the error correlator.
Multiple input, multiple output (MIMO) adaptive control filters are often desirable. Such a MIMO system can have multiple output transducers and/or multiple error sensors and/or multiple input sensors, and has an adaptive control filter with a plurality of adaptive filter channels. Such a MIMO system is described in U.S. Pat. Nos. 5,216,721 and 5,216,722 which are incorporated herein by reference.
The filtered-X LMS and filtered-U RLMS update methods are effective means of providing control for MIMO systems, but the complexity of these methods increases rapidly as the number of input sensors, output transducers, or error sensors grows. For example, a MIMO system having an adaptive FIR (finite impulse response) control filter using the filtered-X LMS update with m reference signals, n output transducers and p error sensors entails the generation of m.times.n.times.p filtered reference signals with p updates per filter channel. Implementing the filtered-X or filtered-U update can easily become computationally burdensome in MIMO applications.
In MIMO applications, it is not always practical to implement the delayed inverse C modeling when using the preferred technique of adaptive on-line C modeling. This is because of difficulties that may be associated with inverting the C model. Also, inverting the C model on-line can be a computational burden. Another problem with delayed inverse C modeling is that inverting the C model inherently skews convergences.
It is therefore desirable to provide an adaptive control system and method that is robust and convergent, yet does not have the drawbacks of delayed inverse C modeling, and is not as computationally burdensome as the filtered-X or filtered-U methods in MIMO applications.